The purpose of this project is to develop computer-aided diagnosis/detection (CAD) for a wide variety of radiologic images and disease types. This project uses existing NIH radiology images. We are developing techniques for segmentation of abdominal CT images to accurately locate the boundaries of the major abdominal organs such as the liver, spleen, adrenal glands, kidneys and pancreas. We made further progress on this project, providing accurate localization and measurement of the pancreas. We made further progress on a project to develop computer-aided detection of prostate cancer on endorectal coil MRI scans. We also developed a Universal Lesion Detector for CT scans. We are developing convolutional neural networks based methods (deep learning) on big data to train computers to detect diseases on radiology images like X-Ray, CT and MRI scans.